报告主题：目标配准与形状分析的整体框架
报告人：Anuj Srivastava 教授 （Florida State University）
报告时间：2017年 9月25日（周一）15:00
报告地点：校本部东区计算机楼402
邀请人：应时辉
报告摘要： In statistical analysis of shapes of objects, an important step is registration. The registration not only preserves important structures in the data but also leads to more parsimonious statistical models for capturing shape variability. In case of parameterized curves and surfaces, the registration step is akin to removing the parameterization variability present in mathematical representations of these objects. Taking three fundamentally different examples: (1) real-valued function data, (2) parameterized curves in Euclidean spaces, and (3) parameterized surfaces in R3, I will describe a comprehensive Riemannian framework that achieves the following goals. It provides an analysis of shapes of curves and surfaces that is invariant to standard similarity transformations and, additionally, to parameterizations of these objects. This framework, called elastic shape analysis, incorporates an optimal registration of points across objects while providing proper metrics, geodesics, and sample statistics of shapes. These sample statistics are further useful in statistical modeling of shapes in different shape classes. I will demonstrate these ideas using applications from medical image analysis, protein structure analysis, 3D face recognition, and human activity recognition in videos.
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